Abstract
Constraint-based search techniques have gained increasing attention in recent years as a basis for scheduling procedures that are capable of accommodating a wide range of constraints. Among these, the Large Neighborhood Search (lns) has largely proven to be a very effective heuristic-based methodology. Its basic optimization cycle consists of a continuous iteration of two steps where the solution is first relaxed and then re-constructed. In Constraint Programming terms, relaxing entails the retraction of some previously imposed constraints, while re-constructing entails imposing new constraints, searching for a better solution. Each iteration of constraint removal and re-insertion can be considered as the examination of a large neighborhood move, hence the procedure’s name. Over the years, LNS has been successfully employed over a wide range of different problems; this paper intends to provide an overview of some utilization examples that demonstrate both the versatility and the effectiveness of the procedure against significantly difficult scheduling benchmarks known in literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Beck, J.C., Davenport, A.J., Davis, E.D., Fox, M.S.: The ODO project: toward a unified basis for constraint-directed scheduling. Journal of Scheduling 1(2), 89–125 (1998)
Baptiste, P., Pape, C.L., Nuijten, W.: Constraint-Based Scheduling, 1st edn. Springer US (2001), doi:10.1007/978-1-4615-1479-4
Ben Hmida, A., Haouari, M., Huguet, M.J., Lopez, P.: Discrepancy search for the flexible job shop scheduling problem. Computers & Operations Research 37, 2192–2201 (2010)
Bożejko, W., Uchroński, M., Wodecki, M.: Parallel meta2heuristics for the flexible job shop problem. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS (LNAI), vol. 6114, pp. 395–402. Springer, Heidelberg (2010)
Cesta, A., Oddi, A., Smith, S.F.: Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems. In: 17th National Conference on Artificial Intelligence, AAAI/IAAI, pp. 742–747 (2000)
Cesta, A., Oddi, A., Smith, S.F.: A constraint-based method for project scheduling with time windows. J. Heuristics 8(1), 109–136 (2002)
Cheng, C., Smith, S.F.: Generating Feasible Schedules under Complex Metric Constraints. In: Proceedings of the 12th National Conference on AI, AAAI 1994 (1994)
Fox, M.S.: Constraint Guided Scheduling: A Short History of Scheduling Research at CMU. Computers and Industry 14(1-3), 79–88 (1990)
Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research 35, 2892–2907 (2008)
Godard, D., Laborie, P., Nuitjen, W.: Randomized Large Neighborhood Search for Cumulative Scheduling. In: Proceedings of the 15th International Conference on Automated Planning & Scheduling, ICAPS 2005, pp. 81–89 (2005)
Hoos, H.H., Stützle, T.: Stochastic Local Search. Foundations and Applications. Morgan Kaufmann (2005)
Laborie, P., Ghallab, M.: Planning with Sharable Resource Constraints. In: Proceedings of the 14th Int. Joint Conference on Artificial Intelligence, IJCAI 1995 (1995)
Laurent, M., Van Hentenryck, P.: Iterative Relaxations for Iterative Flattening in Cumulative Scheduling. In: Proceedings of the 14th International Conference on Automated Planning & Scheduling, ICAPS 2004, pp. 200–208 (2004)
Mastrolilli, M., Gambardella, L.M.: Effective neighbourhood functions for the flexible job shop problem. Journal of Scheduling 3, 3–20 (2000)
Montanari, U.: Networks of Constraints: Fundamental Properties and Applications to Picture Processing. Information Sciences 7, 95–132 (1974)
Moon, H.J., Chi, Y., Hacigümüş, H.: Performance evaluation of scheduling algorithms for database services with soft and hard slas. In: Proceedings of the Second International Workshop on Data Intensive Computing in the Clouds, DataCloud-SC 2011, pp. 81–90. ACM, New York (2011), http://doi.acm.org/10.1145/2087522.2087536
Neumann, K., Schwindt, C.: Activity-on-Node Networks with Minimal and Maximal Time Lags and Their Application to Make-to-Order Production. Operation Research Spektrum 19, 205–217 (1997)
Oddi, A., Rasconi, R.: Solving resource-constrained project scheduling problems with time-windows using iterative improvement algorithms (2009), http://aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/view/736/1142
Oddi, A., Rasconi, R., Cesta, A., Smith, S.F.: Iterative flattening search for the flexible job shop scheduling problem. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, Barcelona, Catalonia, Spain, July 16-22, pp. 1991–1996 (2011), http://ijcai.org/papers11/Papers/IJCAI11-332.pdf
Oddi, A., Smith, S.F.: Stochastic Procedures for Generating Feasible Schedules. In: Proceedings 14th National Conference on AI (AAAI 1997) (1997)
Policella, N., Cesta, A., Oddi, A., Smith, S.F.: From Precedence Constraint Posting to Partial Order Schedules. AI Communications 20(3), 163–180 (2007)
Rossi, A., Dini, G.: Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robotics and Computer-Integrated Manufacturing 23(5), 503–516 (2007)
Sadeh, N.: Look-ahead Techniques for Micro-opportunistic Job-Shop Scheduling. School of Computer Science, Carnegie Mellon University (1991)
Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998), http://dx.doi.org/10.1007/3-540-49481-2_30
Son, S.H., Park, S.: A Priority Based Scheduling Algorithm for Real-Time Databases. Journal of Information Science and Engg. 11(2), 233–248 (1995)
Smith, S.: OPIS: A Methodology and Architecture for Reactive Scheduling. In: Zweben, M., Fox, M. (eds.) Intelligent Scheduling. Morgan Kaufmann (1994)
Thiele, M., Bader, A., Lehner, W.: Multi-objective scheduling for real-time data warehouses. Computer Science - Research and Development 24(3), 137–151 (2009), http://dx.doi.org/10.1007/s00450-009-0062-z
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rasconi, R., Oddi, A., Cesta, A. (2015). Surveying the Versatility of Constraint-Based Large Neighborhood Search for Scheduling Problems. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-18422-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-18422-7_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18421-0
Online ISBN: 978-3-319-18422-7
eBook Packages: Computer ScienceComputer Science (R0)